Illuminating humanist nature in teaching translation and interpreting studies: Devising an online customisable AI-driven subtitling course

The increasing integration of artificial intelligence (AI) in translation and interpreting studies (TIS) necessitates a balanced approach that preserves the humanistic core of the field. While AI tools offer benefits, neglecting the hermeneutic capacity of human translators risks diminishing the essence of communication. This study focuses on subtitling, a less-explored area within TIS, and aims to address the growing need for online courses teaching AI-driven subtitling in the context of the COVID-19 pandemic and the proliferation of audiovisual materials. The study addresses two research questions: (1) the suitability of comparing short video platforms and online translation/interpreting courses for analysis, and (2) how this comparison helps devise a learner-led, AI-driven subtitling online course. The course aims to bridge the gap between technological advancements and the humanistic approach to translation, equipping students with practical skills for real-life subtitling practice.

The theoretical framework draws on polysystem theory, multimodality, and the humanistic nature of translation studies. Polysystem theory, as applied to audiovisual translation (AVT), highlights the dynamic interplay of semiotic systems within a cultural sphere, emphasizing the importance of considering political, economic, social, and cultural factors. Multimodality focuses on the interaction of various semiotic resources in meaning-making, including visual and audio markers in subtitling. The study also aligns with Ye's (2020) argument for maintaining the 'hard core' of human nature in TIS, emphasizing the hermeneutic capacity of language users and the creative, active role of subtitlers in recreating meaning, not just passively reflecting the source text. The humanistic value is emphasized in the course design through the selection of thematically positive materials and a focus on fostering a human community with a shared future.

The study employed a mixed-methods approach. A questionnaire was administered to 36 postgraduate translation and interpreting students to assess their preferences for personalized subtitling settings. The results informed the design of a customized subtitling course. Case studies of three platforms—Douyin (and its Jisu and Huoshan versions), Consecutive Interpreting MOOC, and Working with Translation: Theories and Practice MOOC—were analyzed to understand successful features applicable to the proposed course. Douyin’s different versions (with varying speeds, economic incentives, and interface designs) provided insights into user-friendly features. Consecutive Interpreting MOOC showed a structure with high interactivity and connectivity while offering useful assessment methods. Working with Translation MOOC demonstrated successful aspects of instructional design through video presentations, quizzes, and gamification.

The questionnaire revealed that 63.89% of students favored more personalized subtitling settings, with preferences for options like different subtitle types (bilingual, interlingual, intralingual), adjustable size and speed, and glossaries. Analysis of Douyin highlighted the importance of technological features (smaller installation package, less bandwidth, faster speed), economic incentives (bonus money for both creators and viewers), and a clutter-free interface for enhancing user experience. The Consecutive Interpreting MOOC demonstrated effective interactive elements such as student demonstrations and peer feedback, while the Working with Translation MOOC showed successful gamification and interactive discussion forums. The findings suggest that a successful online subtitling course should integrate AI-driven technologies while allowing for customization of settings, including subtitle type, size, speed, and language, and providing economic incentives for student participation.

The proposed online, AI-driven subtitling course integrates the findings from the questionnaire and case studies, creating a learner-centric environment. The course emphasizes the importance of thematically positive content, aligning with the humanistic emphasis. The structure, assessment style, and interactive elements draw from the successful features of the analyzed MOOCs, while the personalization options cater to diverse learner needs and preferences. The course's focus on producing technologically advanced, localized, and aesthetically pleasing subtitles connects the technical and humanistic aspects, bridging theory and practice. The integration of economic incentives motivates students and aligns with the concept of a creative economy.

The study successfully devised a framework for an online, customizable AI-driven subtitling course that prioritizes both technological efficiency and the humanistic essence of translation. The course integrates AI-driven tools with personalized settings, economic incentives, and interactive elements to foster engaging and effective learning. Future research could explore the course’s effectiveness in practice and investigate further the integration of other AI tools and interactive elements.

The study's sample size for the questionnaire was relatively small, limiting the generalizability of the findings. The case studies focused primarily on Chinese platforms, potentially limiting the applicability of the findings to other cultural contexts. Further research is needed to evaluate the course's long-term impact on student learning and professional development.